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Recently, edge computing is getting attention as a new computing paradigm that is expected to achieve short-delay and high-throughput task offloading for large scale Internet-of-Things (IoT) applications. In edge computing, workload distribution is one of the most critical issues that largely influences the delay and throughput performance of edge clouds, especially in distributed Function-as-a-Service (FaaS) over networked edge nodes. In this paper, we propose the Resource Allocation Control Engine with Reinforcement learning (RACER), which provides an efficient workload distribution strategy to reduce the task response slowdown with per-task response time Quality-of-Service (QoS). First, we present a novel problem formulation with the per-task QoS constraint derived from the well-known token bucket mechanism . Second, we employ a problem relaxation to reduce the overall computation complexity by compromising just a bit of optimality. Lastly, we take the deep reinforcement learning approach as an alternative solution to the workload distribution problem to cope with the uncertainty and dynamicity of underlying environments. Evaluation results show that RACER achieves a significant improvement in terms of per-task QoS violation ratio, average slowdown, and control efficiency, compared to AREA, a state-of-the-art workload distribution method.

As the data size on smart things continue to grow, it becomes inefficient to transfer the massive raw data to centralized cloud datacenters for the analysis of raw data and the deployment of innovative and novel services into various areas such as smart cities, etc. As the concept of Fog Computing, the structure of distributed intelligent infrastructure can address those problems by providing elastic resources and services to end users with smart devices at the edge of network. Hence, through the functions of analytics and flexible control based on the Software-Defined Network (SDN), it is necessary to provide the SDN-driven management intelligence, which can maximize network utilization subject to constraints on service priority and resources fairness by differentiating service flows according to the quality requirements. This paper proposes the SDN controller for intelligent inter-datacenter (inter-DC) cloud networking, which enables optimal traffic loads distribution across the distributed datacenters with the global inter-DC view of link states and flow statistics by applying the network-aware flow scheduling on a policy-driven inter-DC traffic control layer. The proposed intelligent inter-DC controller performs a global resources scheduling mechanism that uses different weights according to the priority of service class in allocating bandwidths based on multi-layers networking interconnecting the distributed datacenters.

User mobility characteristics are considered to significantly affect the performance and UX of modern IT systems such as mobile communications, context-aware services, location-based applications, smart mobility, and so on. Due to privacy concerns, legal issues, and expensive measurement costs for real user mobility traces, synthetic mobility traces are widely used for research and development purposes. Until now, most of the user mobility synthesis approaches have been categorized into two major paradigms: stochastic modeling and simulation. However, they naturally have a limitation in mimicking actual user movements because it is impossible to capture all temporal, spatial, and behavioral characteristics that are implicitly melted in real user mobility. Along with the recent advancements of deep learning technologies, there have been several challenging proposals that exploit the power of the deep learning, especially generative models, in mobility synthesis. This paper reviews and summarizes recently proposed user mobility synthesis schemes based on generative adversarial networks that have been one of the most leading deep learning technologies for the last few years.

The performance of Functions-as-a-Service (FaaS) would be significantly improved by organizing cloud servers into a hierarchical distributed architecture, resulting in low-latency access and faster response when compared to centralized cloud. However, the distributed organization introduces a new type of decision making problem for placing and executing functions to a specific cloud server. In order to handle the problem, we extended a well-known cloud computing simulator, CloudSim. The extended CloudSim enables users to define FaaS functions with various characteristics and service level objectives (SLOs), place them across geo-distributed cloud servers, and evaluate per-function performance. Proof-of-Concept (PoC) evaluation results show the potential of our CloudSim extension in terms of execution efficiency and simulation reality.

Now, SDN (Software Defined Networking) has been accepted as an unavoidable networking architecture since the ONF (Open Networking Forum) foundation, the release of Google's B4 project, and several respective deployments of cloud and transport SDNs. In addition, SDN has been investigated for a basic networking infrastructure for the 5G/IoT's era and an integration between SDN based cloud and transport networks. Through the shift of networking paradigm to SDN, this paper describes the overall R&D processes including requirements, architecture, implementation, and testbed for the SDN orchestration over distributed cloud networks connected through transport networks, as a part of effort to realize the core technologies of smart network infrastructure.

The QoS requirements of various services have created a big challenge in the virtualization environments for cloud networking that cannot support the fine-grained network QoS management. As an emerging technology for satisfying these requirements, the Software Defined Networking (SDN) has the potential to enable dynamic configuration and control for the enhanced network management. This paper presents the design of an intelligent controller based on network policy in the environments of virtual networks that hide the details of an underlying physical topology. The proposed controller performs the mechanism for policy-driven intelligent control to provide differentiated virtual network services based on the content of an OpenFlow packet header and mapping relationships between physical infrastructure and virtual overlay network. With this approach, the intelligent controller enables the deployment of various services on multiple virtual networks as well as the flexible management for achieving a better QoS. Furthermore, it can distribute the traffic with different QoS requirements on available paths under the constraints of available resources by adopting the structure for enforcing the QoS policy of flows.

A distinct trend has emerged that the Internet is used to transport data on a more and more massive scale. Capacity shortage in the backbone networks has become a genuine possibility, which will be more serious with fiber-based access. The problem addressed in this paper is how to conduct massive content distribution efficiently in the future network environment, where the capacity limitation can equally be at the core or the edge. We propose a novel technique as a main content transport mechanism to achieve efficient network resource utilization. The technique uses multiple trees for distributing different file pieces, which at the heart is a version of swarming. In this paper, we formulate an optimization problem for determining an optimal set of distribution trees as well as the rate of distribution on each tree under bandwidth limitation at arbitrary places in the network. The optimal solution can be found by a distributed algorithm. The results of the paper not only provide stand-alone solutions to the massive content distribution problem, but should also help the understanding of existing distribution techniques such as BitTorrent or FastReplica.

We consider a two-tier content distribution system for distributing massive content, consisting of an infrastructure content distribution network (CDN) and a large number of ordinary clients. The nodes of the infrastructure network form a structured, distributed-hash-table-based (DHT) peer-to-peer (P2P) network. Each file is first placed in the CDN, and possibly, is replicated among the infrastructure nodes depending on its popularity. In such a system, it is particularly pressing to have proper load-balancing mechanisms to relieve server or network overload. The subject of the paper is on popularity-based file replication techniques within the CDN using multiple hash functions. Our strategy is to set aside a large number of hash functions. When the demand for a file exceeds the overall capacity of the current servers, a previously unused hash function is used to obtain a new node ID where the file will be replicated. The central problems are how to choose an unused hash function when replicating a file and how to choose a used hash function when requesting the file. Our solution to the file replication problem is to choose the unused hash function with the smallest index, and our solution to the file request problem is to choose a used hash function uniformly at random. Our main contribution is that we have developed a set of distributed, robust algorithms to implement the above solutions and we have evaluated their performance. In particular, we have analyzed a random binary search algorithm for file request and a random gap removal algorithm for failure recovery.

Traffic monitoring is the essential capability for large-scale enterprises, data centers, service providers, and network operators to ensure reliability, availability, fault assurance, and security of their underlying network resources. Currently, most monitoring solutions are standalone dedicated ones. Major drawbacks of these dedicated standalone appliances per-feature are high-cost, lack of flexibility, slow install time and difficulty of maintenance. Network Function Virtualization (NFV) provides an attractive alternative to cope with such limitations by controlling both CAPEX and OPEX. Network traffic monitoring function virtualization brings not only CAPEX/OPEX advantages but also introduces some challenges such as ensuring scalability and performance of single or distributed multiple virtual monitoring functions, utilization of virtual functions, and flexibility and easiness of virtual functions lifecycle management. To address such challenges, we propose a novel architecture and proof-of-concept implementation of a software-defined Virtual TrAffic Monitoring function with resource Auto-Scaling capability built over a multi-core whitebox server (VTAMAS) in this paper. It virtualizes monitoring functions with the capability of auto-scaling resource of virtual functions for efficient resource utilization especially.

The paper addresses the problem of massive content distribution in a network where multiple sessions coexist. In more traditional approaches, the sessions form separate overlay networks and operate independently from each other. In this case, some sessions may suffer from insufficient resources (e.g., aggregate upload bandwidth) even though other sessions have excessive resources. To cope with this problem, we consider the universal swarming approach, which allows multiple sessions to cooperate with each other by forming a shared overlay network. We formulate the problem of finding the optimal resource allocation to maximize the sum of the session utilities under the network capacity constraints. The solution turns out to be optimal sharing of multiple minimum-cost multicast trees. We present a subgradient algorithm and prove that, although the algorithm uses a single multicast tree per session at each iteration and hence does not converge in the conventional sense, it converges to the optimal solution in the time-average sense. The solution involves an NP-hard subproblem of finding a minimum-cost Steiner tree. We cope with this difficulty by using a column generation method, which reduces the number of Steiner-tree computations. Furthermore, we allow the use of approximate solutions to the Steiner-tree subproblem. We show that the approximation ratio to the overall problem turns out to be no more than that to the Steiner-tree subproblem. Simulation results demonstrate that universal swarming improves the performance of resource-poor sessions with negligible impact to resource-rich sessions.

We propose a residential gateway architecture for IPTV service that has QoS management functionality. The proposed residential gateway contains a multicast packet classifier and a priority regenerator that can handle multiple IPTV traffics concurrently. We also implement an IP tuner for an IP settop box that can extract MPEG streams from IP packets and has the same role as a legacy tuner of a digital TV settop box. In order to provide IPTV service with QoS in a home network, we design an IPTV channel zapping manager that communicates coincidently with a home network QoS manager.

This paper presents a new IGMP multicast support system for wireless highspeed portable Internet (HPi) networks. The Internet group management protocol (IGMP) is common standard session-layer protocol for IP multicasting on the Internet. However, the network topology and the L2 protocol of the HPi make it difficult to adopt the IGMP on HPi networks. We propose the use of an IGMP proxy for multicast services because it provides the way of joining a multicast group over IEEE 802.16e and assists the fast multicast rejoining operations during handoff. Finally, using the multicast delay model, we present numerical results showing the effect of the fast multicast rejoining method.

We propose a home gateway architecture for an IPTV service that has QoS management functionality. This home gateway contains a multicast packet classifier and a priority regenerator that can handle multiple IPTV traffics concurrently. In order to provide IPTV service with QoS in a home network, we also design an IPTV channel zapping manager that communicates coincidently with a home network QoS manager.

Reducing the channel zapping time is critical problem to resolve for the spread of the IPTV service. In general, channel change times depends on several Parameters: command processing time, network delay time, STB layer delay time, STB jitter buffer delay time and MPEG decoder time. We focus on the network delay time, which does not exist in the traditional broadcasting service such as terrestrial, cable, or satellite broadcasting service. This could weaken the spread of IPTV service. In this paper we present the method of improving the channel zapping time by making adjacent multicast groups of current channel come to home gateway in advance. When IP STB requests join to new channel by sending IGMP membership report message, home gateway sends not only IGMP membership report message for the channel but also for the adjacent channels. Therefore multicast streams for the adjacent channels of current one always come to home gateway. When IP STB requests join to an adjacent channel, home gateway can forward the corresponding multicast stream immediately.